Dr. Khan is an international leader in Big Data Analytics (BDA), a key aspect of data science. He has been instrumental in educating the students in BDA at UTD, for several years. He was also successful in writing multiple proposals to NSF and getting funding for experimental research and introduced his BDA students to research projects. His BDA class has often had enrollments of around 130 and is extremely popular. He has also developed a BDA framework and collaborates with faculty at EPPS (e.g., Profs. Jennifer Holmes and Patrick Brandt) and shares his framework with them for their applications in political science. Together with EPPS professors as well as through other collaborations (e.g., UIUC, U of MN) he has brought in millions of dollars in federal funding in this area. He is known worldwide as the Data Science person at UTD by eminent researchers.
Dr. Khan's research is balanced. He is an internationally recognized authority in stream data mining fundamentals and applications in cybersecurity and scalable complex data analytics. He pioneered the development of many novel algorithms, frameworks and performance-driven approaches in these areas. He develops a number of novel approaches, supported by mathematical rigors and demonstrates superiority of his approaches over baselines with experimental results. More specifically, his group has done significant research on machine learning (ML)/data mining, data analytics in cyber security, real-time anomaly detection over evolving streams, vulnerability analysis of malware apps for smart phones, encrypted traffic analysis, and secure encrypted stream data processing using modern secure hardware extensions.